Analysis of Competitive and Cooperative Technology Strategies of Electronics Firms in the Greater China Region

This paper integrates several theoretical perspectives to discuss the attributes of successful implementing strategic alliances and supply chain management strategies in high-technology industries. A multiple- case study of Taiwanese and Chinese electronics industries is presented to demonstrate how and why different firms apply different technology strategies in alliances and supply chain formations. Due to intense global competition, technological integration, and product life-cycle time compression, Taiwanese and Chinese high-technology firms are suggested to formulate and implement a coherent technology strategy to enhance their global competitiveness. By applying an integrated framework based on major theoretical perspectives studying the formulation and implementation of competitive and cooperative strategies, the results of this multiple-case study concludes that six closely related strategies, i.e., supply chain positioning, operation efficiency, strategic motives, resource complementarity, organizational learning and capabilities, and strategic flexibility, can be employed by business executives in formulating alliances and supply chain strategies. The research findings serve as an illustration of the multi-dimensionality and complexity of alliance strategies. The framework also provides a useful start to better understanding the dynamic nature of formulating competitive and cooperative technology strategies and to facilitate the effective evaluation of the conditions under these strategies might achieve optimal results

Harnessing Deep Q-Learning for Enhanced Statistical Arbitrage in High-Frequency Trading: A Comprehensive Exploration

The realm of High-Frequency Trading (HFT) is characterized by rapid decision-making processes that capitalize on fleeting market inefficiencies. As the financial markets become increasingly competitive, there is a pressing need for innovative strategies that can adapt and evolve with changing market dynamics. Enter Reinforcement Learning (RL), a branch of machine learning where agents learn by interacting with their environment, making it an intriguing candidate for HFT applications. This paper dives deep into the integration of RL in statistical arbitrage strategies tailored for HFT scenarios. By leveraging the adaptive learning capabilities of RL, we explore its potential to unearth patterns and devise trading strategies that traditional methods might overlook. We delve into the intricate exploration-exploitation trade-offs inherent in RL and how they manifest in the volatile world of HFT. Furthermore, we confront the challenges of applying RL in non-stationary environments, typical of financial markets, and investigate methodologies to mitigate associated risks. Through extensive simulations and backtests, our research reveals that RL not only enhances the adaptability of trading strategies but also shows promise in improving profitability metrics and risk-adjusted returns. This paper, therefore, positions RL as a pivotal tool for the next generation of HFT-based statistical arbitrage, offering insights for both researchers and practitioners in the field

Decoding the Mindset: A Neural Network Approach for Analyzing CEO’s Digital Strategy and Its Innovation Implications

The swift advancement of digital technologies demands CEOs to prioritize digital innovation strategies to stay competitive. However, an overemphasis on digitality, neglecting aspects like customer focus, operations, and collaboration, can hinder innovation. Using a neural network, we evaluated CEOs’ digital strategies by training on 1,000 company pitches and applying this to S&P 500 CEOs\u27 Shareholder Letters (2001-2018). We discovered an inverted U relationship between digital strategy intensity and innovation performance. This stresses the need for a balanced strategy with the right digital focus. Our research illuminates top executives\u27 digital mindset in driving innovation, emphasizing the potential pitfalls of a purely digital approach. Furthermore, our machine-learning method offers a novel, scalable way to quantify digital strategy, paving the way for subsequent research

Playing Stackelberg Opinion Optimization with Randomized Algorithms for Combinatorial Strategies

From a perspective of designing or engineering for opinion formation games in social networks, the "opinion maximization (or minimization)" problem has been studied mainly for designing subset selecting algorithms. We furthermore define a two-player zero-sum Stackelberg game of competitive opinion optimization by letting the player under study as the first-mover minimize the sum of expressed opinions by doing so-called "internal opinion design", knowing that the other adversarial player as the follower is to maximize the same objective by also conducting her own internal opinion design. We propose for the min player to play the "follow-the-perturbed-leader" algorithm in such Stackelberg game, obtaining losses depending on the other adversarial player's play. Since our strategy of subset selection is combinatorial in nature, the probabilities in a distribution over all the strategies would be too many to be enumerated one by one. Thus, we design a randomized algorithm to produce a (randomized) pure strategy. We show that the strategy output by the randomized algorithm for the min player is essentially an approximate equilibrium strategy against the other adversarial player

Understanding and managing the manage processes

This paper discusses understanding and managing the manage processes. It was presented at the conference of the Performance Measurement Association in 2004

Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence

Learning agents that are not only capable of taking tests, but also innovating is becoming a hot topic in AI. One of the most promising paths towards this vision is multi-agent learning, where agents act as the environment for each other, and improving each agent means proposing new problems for others. However, existing evaluation platforms are either not compatible with multi-agent settings, or limited to a specific game. That is, there is not yet a general evaluation platform for research on multi-agent intelligence. To this end, we introduce Arena, a general evaluation platform for multi-agent intelligence with 35 games of diverse logics and representations. Furthermore, multi-agent intelligence is still at the stage where many problems remain unexplored. Therefore, we provide a building toolkit for researchers to easily invent and build novel multi-agent problems from the provided game set based on a GUI-configurable social tree and five basic multi-agent reward schemes. Finally, we provide Python implementations of five state-of-the-art deep multi-agent reinforcement learning baselines. Along with the baseline implementations, we release a set of 100 best agents/teams that we can train with different training schemes for each game, as the base for evaluating agents with population performance. As such, the research community can perform comparisons under a stable and uniform standard. All the implementations and accompanied tutorials have been open-sourced for the community at https://sites.google.com/view/arena-unity/

Identify successful marketing communication strategies that apply to a small hair salon

A selected organisation provides hair dressing services and hair products to customers. The aim of this report is to analyse how the small hair salon could improve their marketing communication strategies in order to attract more customers and enhance the relationship between customers and the organisation. The approach to collecting information was to use a questionnaire with 50 participants, to gather primary information and to conduct a secondary research study. The result of this research was to decide that the role of a successful marketing communication strategy is to attract the customer to consume. In order to make the marketing communication strategy successful, it needs to choose a suitable channel that enables it to connect with the customer. New media is an effective channel that can promote the business to the customer and interact with them. New media is also suitable for a small business to use. A recommendation for the organisation is they create their own website page, Facebook page, YouTube video and WeChat group to promote themselves and interact with customers. Those channels are popular in New Zealand, with a high number of active users. Most the organisation customers like to use those channels too, so if the organisation applies those channels to their marketing communication strategy they will be able to attract customers and persuade them to consume more products

Examining the implications of the anti-money laundering and countering financing of Terrorism Act 2009 on New Zealand accounting firms

Money laundering is the act of introducing illicitly gained funds into the economy to assist in concealing their origin. On October 1 2018, it became mandatory for most New Zealand accounting firms to comply with the Anti-Money Laundering and Countering Financing of Terrorism Act 2009. The purpose of this act is to help detect and deter money laundering within New Zealand. The AML/CFT Act creates additional requirements for accounting firms and has severe penalties for non-compliance. This led to the research question of ‘What are the implications of the AML/CFT Act 2009 on New Zealand Accounting firms?’ For this research, interviews were conducted with accounting firms to help identify the costs and implications associated with the AML/CFT requirements. The results revealed that despite the October 1 deadline, accounting firms are still implementing programs. The new requirements were unclear and underestimated by firms. Large money and time costs were reported by all the interview participants and they all feel that the new requirements are excessive. As the AML/CFT Act is still new, it would be beneficial to explore further research in the future that examines the actual impact of maintaining the AML/CFT programs